---
license: apache-2.0
datasets:
- AadityaJain/Fromula_text_classification
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Formula-Text-Detection
- SigLIP2
- Image-Classification
---

![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/lg90wKzVcHjnTXs8_EGCR.png)

# **Formula-Text-Detection**

> **Formula-Text-Detection** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is built using the **SiglipForImageClassification** architecture to distinguish between **mathematical formulas** and **natural text** in document or image regions.

> [!Note]
> Note: This model works best with plain text or formulas using the same font style


```py
Classification Report:
              precision    recall  f1-score   support

     formula     0.9983    1.0000    0.9991      6375
        text     1.0000    0.9980    0.9990      5457

    accuracy                         0.9991     11832
   macro avg     0.9991    0.9990    0.9991     11832
weighted avg     0.9991    0.9991    0.9991     11832
```

![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/OdNUMSb_utc_RBWd3Gjfq.png)

---

> [!note]
*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786

---

## **Label Space: 2 Classes**

The model classifies each input image into one of the following categories:

```
Class 0: "formula"
Class 1: "text"
```

---

## **Install Dependencies**

```bash
pip install -q transformers torch pillow gradio
```

---

## **Inference Code**

```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Formula-Text-Detection"  # Replace with your model path if different
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Label mapping
id2label = {
    "0": "formula",
    "1": "text"
}

def classify_formula_or_text(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()

    prediction = {
        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
    }

    return prediction

# Gradio Interface
iface = gr.Interface(
    fn=classify_formula_or_text,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2, label="Formula or Text"),
    title="Formula-Text-Detection",
    description="Upload an image region to classify whether it contains a mathematical formula or natural text."
)

if __name__ == "__main__":
    iface.launch()
```
## **Demo Inference**

> [!Important]
> Text


![Screenshot 2025-04-30 at 04-57-23 Formula-Text-Detection.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KulSEk6AEV-QgMX4rFimq.png)
![Screenshot 2025-04-30 at 04-57-50 Formula-Text-Detection.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/3y1nWn2moOgga939LlhzB.png)
![Screenshot 2025-04-30 at 04-58-16 Formula-Text-Detection.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/xvliPSGTHtA_bkEl5utIM.png)

> [!Important]
> Formula

![Screenshot 2025-04-30 at 04-58-51 Formula-Text-Detection.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/4TbEz_vLKochuTuNiq7cH.png)
![Screenshot 2025-04-30 at 04-59-28 Formula-Text-Detection.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/fS_EoLZ7pnfWoWB5pVooL.png)
![Screenshot 2025-04-30 at 05-01-42 Formula-Text-Detection.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/utc88h1KQLLXKB-qllT4v.png)

---

## **Intended Use**

**Formula-Text-Detection** can be used in:

- **OCR Preprocessing** – Improve document OCR accuracy by separating formulas from text.
- **Scientific Document Analysis** – Automatically detect mathematical content.
- **Educational Platforms** – Classify and annotate scanned materials.
- **Layout Understanding** – Help AI systems interpret mixed-content documents.